We are aware of no previous research that focused on understanding
IoT security discussions in SO.
In SE, topic modeling is used to learn aspects like software logging [1]},
feature location [2]}, [3]},
traceability linking [4]}, [5]},
software and source code evolution [6]}, [7]}, [8]},
source code categorization [9]}, code refactoring [10]},
defect analysis [11]}, and various software maintenance
tasks [12]}, [13]}.
The SO posts are subject to topic modeling to understand concurrency [14]}, big
data [15]} and chatbot issues [16]}.
Yang et al. [17]} studied security-topics in SO. They used SO tags to identify mobile security which might miss many security discussions which were not tagged as any security related tags. Our studies resolve this issue by following Uddin et al. [18]} to collect all IoT posts. Next, they applied topic modeling to the security posts. As the recent studies have found that larger documents may have multiple topics, LDA topic modeling is unstable for such cases [19]}. We thus use sentence level topic modeling. We develop a precise security detector, SecBot, to identify security related sentences from SO posts and apply LDA topic modeling on our security dataset. Unlike Yang et al. [17]}, we focus on IoT security topics.
